Abstract

In this paper, we develop a generative model to describe
the layouts of outdoor scenes – the spatial configuration of
regions. Specifically, the layout of an image is represented
as a composite of regions, each associated with a semantic
topic. At the heart of this model is a novel stochastic
process called Spatial Topic Process, which generates
a spatial map of topics from a set of coupled Gaussian processes,
thus allowing the distributions of topics to vary continuously
across the image plane. A key aspect that distinguishes
this model from previous ones consists in its capability
of capturing dependencies across both locations and
topics while allowing substantial variations in the layouts.
We demonstrate the practical utility of the proposed model
by testing it on scene classification, semantic segmentation,
and layout hallucination.